虚拟化技术的广泛应用

Extensive application virtualization technology

(4) Empirical analysis of four sets of data comprising a total of 33 different data series (the classic Medical Innovation data, 19 music CDs, five high-tech products, and eight miscellaneous innovations) indicates that the two-segment model fits markedly better than the MIM, the G/SG, and the WG models, at least for innovations for which a two-segment structure is likely to exist. Hence, the model does better when it is theoretically expected to, and does not when it is not theoretically expected to. The two-segment model fits about equally well as the mixed-influence model proposed by Karmeshu and Goswami (2001), where p and q vary in a continuous fashion. Overall, the findings on descriptive performance are robust to changes in the error structure and indicate that the discrete-mixture model is sufficiently different and the data sufficiently informative for the model to fit real data better than other models. The model we presented provides sharper insight into how social structure can affect macro-level diffusion patterns, and should prove useful in five areas of application where influentials and imitators are a priori likely to exist. The first two are high-techrology

Wide application of virtualization technology<BR>